{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import quandl\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
    "goog_table = quandl.get('WIKI/GOOG')\n",
    "amzn_table = quandl.get('WIKI/AMZN')\n",
    "ebay_table = quandl.get('WIKI/EBAY')\n",
    "wal_table = quandl.get('WIKI/WMT')\n",
    "aapl_table = quandl.get('WIKI/AAPL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>goog</th>\n",
       "      <th>amzn</th>\n",
       "      <th>ebay</th>\n",
       "      <th>wal</th>\n",
       "      <th>aapl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>-0.001708</td>\n",
       "      <td>-0.007531</td>\n",
       "      <td>0.008427</td>\n",
       "      <td>-0.000719</td>\n",
       "      <td>0.001976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>0.002074</td>\n",
       "      <td>0.014113</td>\n",
       "      <td>0.014993</td>\n",
       "      <td>0.002298</td>\n",
       "      <td>0.006331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>-0.008246</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>-0.007635</td>\n",
       "      <td>-0.005611</td>\n",
       "      <td>-0.004273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>-0.002883</td>\n",
       "      <td>-0.009081</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>-0.000722</td>\n",
       "      <td>-0.000257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>-0.014113</td>\n",
       "      <td>-0.020172</td>\n",
       "      <td>-0.009720</td>\n",
       "      <td>-0.002023</td>\n",
       "      <td>-0.007826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                goog      amzn      ebay       wal      aapl\n",
       "Date                                                        \n",
       "2016-12-23 -0.001708 -0.007531  0.008427 -0.000719  0.001976\n",
       "2016-12-27  0.002074  0.014113  0.014993  0.002298  0.006331\n",
       "2016-12-28 -0.008246  0.000946 -0.007635 -0.005611 -0.004273\n",
       "2016-12-29 -0.002883 -0.009081 -0.001000 -0.000722 -0.000257\n",
       "2016-12-30 -0.014113 -0.020172 -0.009720 -0.002023 -0.007826"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goog = goog_table.loc['2016',['Close']]\n",
    "amzn = amzn_table.loc['2016',['Close']]\n",
    "ebay = ebay_table.loc['2016',['Close']]\n",
    "wal = wal_table.loc['2016',['Close']]\n",
    "aapl = aapl_table.loc['2016',['Close']]\n",
    "goog_log = np.log(goog.Close).diff().dropna()\n",
    "amzn_log = np.log(amzn.Close).diff().dropna()\n",
    "ebay_log = np.log(ebay.Close).diff().dropna()\n",
    "wal_log = np.log(wal.Close).diff().dropna()\n",
    "aapl_log = np.log(aapl.Close).diff().dropna()\n",
    "df = pd.concat([goog_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()\n",
    "df.columns = ['goog','amzn','ebay','wal','aapl']\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Close   R-squared:                       0.346\n",
      "Model:                            OLS   Adj. R-squared:                  0.319\n",
      "Method:                 Least Squares   F-statistic:                     12.51\n",
      "Date:                Wed, 23 May 2018   Prob (F-statistic):           9.49e-10\n",
      "Time:                        14:16:24   Log-Likelihood:                 419.66\n",
      "No. Observations:                 124   AIC:                            -827.3\n",
      "Df Residuals:                     118   BIC:                            -810.4\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0004      0.001      0.453      0.651      -0.001       0.002\n",
      "MKT            1.2675      0.200      6.339      0.000       0.872       1.664\n",
      "SMB           -0.4920      0.187     -2.636      0.010      -0.862      -0.122\n",
      "HML           -0.4131      0.185     -2.228      0.028      -0.780      -0.046\n",
      "RMW           -0.1974      0.293     -0.673      0.502      -0.778       0.384\n",
      "CMA           -0.6478      0.283     -2.292      0.024      -1.208      -0.088\n",
      "==============================================================================\n",
      "Omnibus:                       20.018   Durbin-Watson:                   2.022\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               90.152\n",
      "Skew:                          -0.269   Prob(JB):                     2.65e-20\n",
      "Kurtosis:                       7.142   Cond. No.                         410.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = sm.ols(formula = 'amzn~goog+ebay+wal+aapl',data = df).fit()\n",
    "print(model2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   amzn   R-squared:                       0.351\n",
      "Model:                            OLS   Adj. R-squared:                  0.348\n",
      "Method:                 Least Squares   F-statistic:                     134.7\n",
      "Date:                Wed, 23 May 2018   Prob (F-statistic):           3.50e-25\n",
      "Time:                        14:16:25   Log-Likelihood:                 702.38\n",
      "No. Observations:                 251   AIC:                            -1401.\n",
      "Df Residuals:                     249   BIC:                            -1394.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0005      0.001      0.550      0.583      -0.001       0.002\n",
      "goog           0.8636      0.074     11.607      0.000       0.717       1.010\n",
      "==============================================================================\n",
      "Omnibus:                       67.564   Durbin-Watson:                   1.823\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1470.739\n",
      "Skew:                          -0.374   Prob(JB):                         0.00\n",
      "Kurtosis:                      14.835   Cond. No.                         79.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "simple = sm.ols(formula = 'amzn ~ goog',data = df).fit()\n",
    "print(simple.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'\n",
    "fama_table = pd.read_csv(url)\n",
    "index = [datetime.strptime(str(x), \"%Y%m%d\") for x in fama_table.iloc[:,0]]\n",
    "fama_table.index = index\n",
    "fama_table = fama_table.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "fama = fama_table['2016']\n",
    "fama = fama.rename(columns = {'Mkt-RF':'MKT'})\n",
    "fama = fama.apply(lambda x: x/100)\n",
    "fama_df = pd.concat([fama,amzn_log],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Close   R-squared:                       0.387\n",
      "Model:                            OLS   Adj. R-squared:                  0.375\n",
      "Method:                 Least Squares   F-statistic:                     30.97\n",
      "Date:                Wed, 23 May 2018   Prob (F-statistic):           2.21e-24\n",
      "Time:                        14:16:27   Log-Likelihood:                 709.59\n",
      "No. Observations:                 251   AIC:                            -1407.\n",
      "Df Residuals:                     245   BIC:                            -1386.\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0010      0.001      1.028      0.305      -0.001       0.003\n",
      "MKT            0.9612      0.125      7.691      0.000       0.715       1.207\n",
      "SMB           -0.5890      0.182     -3.235      0.001      -0.948      -0.230\n",
      "HML           -0.1335      0.211     -0.632      0.528      -0.549       0.282\n",
      "RMW           -0.4851      0.264     -1.840      0.067      -1.005       0.034\n",
      "CMA           -1.5555      0.324     -4.801      0.000      -2.194      -0.917\n",
      "==============================================================================\n",
      "Omnibus:                       69.457   Durbin-Watson:                   1.937\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2012.675\n",
      "Skew:                           0.241   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.864   Cond. No.                         399.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA',data = fama_df).fit()\n",
    "print(fama_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a160d0128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = pd.DataFrame({'simple regression':simple.predict(),'fama_french':fama_model.predict(),'sample':df.amzn},index = df.index)\n",
    "plt.figure(figsize = (15,7.5))\n",
    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])\n",
    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103c271d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "simple.resid.plot.density()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-136-15922c3fe4e7>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-136-15922c3fe4e7>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'residual mean: ', np.mean(fama_model.resid)\u001b[0m\n\u001b[0m                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "print 'residual mean: ', np.mean(fama_model.resid)\n",
    "print 'residual variance: ', np.var(fama_model.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0bcb26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "plt.scatter(df.goog,simple.resid)\n",
    "plt.axhline(0.05,color = 'r')\n",
    "plt.axhline(-0.05,color = 'r')\n",
    "plt.axhline(0,color = 'black')\n",
    "plt.xlabel('x value')\n",
    "plt.ylabel('residual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.core import datetools\n",
    "from statsmodels.stats import diagnostic as dia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value of Heteroskedasticity:  0.14407584284381386\n"
     ]
    }
   ],
   "source": [
    "het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])\n",
    "print('p-value of Heteroskedasticity: ', het[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.7248088754041377, nan, 1.7298240426802394, 0.18963839548692538)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia.het_breuschpagan(simple.resid,pd.DataFrame(df.goog))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2668279319999998\n"
     ]
    }
   ],
   "source": [
    "print((float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
